2,254 research outputs found
Synthesis versus analysis in patch-based image priors
In global models/priors (for example, using wavelet frames), there is a well
known analysis vs synthesis dichotomy in the way signal/image priors are
formulated. In patch-based image models/priors, this dichotomy is also present
in the choice of how each patch is modeled. This paper shows that there is
another analysis vs synthesis dichotomy, in terms of how the whole image is
related to the patches, and that all existing patch-based formulations that
provide a global image prior belong to the analysis category. We then propose a
synthesis formulation, where the image is explicitly modeled as being
synthesized by additively combining a collection of independent patches. We
formally establish that these analysis and synthesis formulations are not
equivalent in general and that both formulations are compatible with analysis
and synthesis formulations at the patch level. Finally, we present an instance
of the alternating direction method of multipliers (ADMM) that can be used to
perform image denoising under the proposed synthesis formulation, showing its
computational feasibility. Rather than showing the superiority of the synthesis
or analysis formulations, the contributions of this paper is to establish the
existence of both alternatives, thus closing the corresponding gap in the field
of patch-based image processing.Comment: To appear in ICASSP 201
Learning sparse representations of depth
This paper introduces a new method for learning and inferring sparse
representations of depth (disparity) maps. The proposed algorithm relaxes the
usual assumption of the stationary noise model in sparse coding. This enables
learning from data corrupted with spatially varying noise or uncertainty,
typically obtained by laser range scanners or structured light depth cameras.
Sparse representations are learned from the Middlebury database disparity maps
and then exploited in a two-layer graphical model for inferring depth from
stereo, by including a sparsity prior on the learned features. Since they
capture higher-order dependencies in the depth structure, these priors can
complement smoothness priors commonly used in depth inference based on Markov
Random Field (MRF) models. Inference on the proposed graph is achieved using an
alternating iterative optimization technique, where the first layer is solved
using an existing MRF-based stereo matching algorithm, then held fixed as the
second layer is solved using the proposed non-stationary sparse coding
algorithm. This leads to a general method for improving solutions of state of
the art MRF-based depth estimation algorithms. Our experimental results first
show that depth inference using learned representations leads to state of the
art denoising of depth maps obtained from laser range scanners and a time of
flight camera. Furthermore, we show that adding sparse priors improves the
results of two depth estimation methods: the classical graph cut algorithm by
Boykov et al. and the more recent algorithm of Woodford et al.Comment: 12 page
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Multi-Content GAN for Few-Shot Font Style Transfer
In this work, we focus on the challenge of taking partial observations of
highly-stylized text and generalizing the observations to generate unobserved
glyphs in the ornamented typeface. To generate a set of multi-content images
following a consistent style from very few examples, we propose an end-to-end
stacked conditional GAN model considering content along channels and style
along network layers. Our proposed network transfers the style of given glyphs
to the contents of unseen ones, capturing highly stylized fonts found in the
real-world such as those on movie posters or infographics. We seek to transfer
both the typographic stylization (ex. serifs and ears) as well as the textual
stylization (ex. color gradients and effects.) We base our experiments on our
collected data set including 10,000 fonts with different styles and demonstrate
effective generalization from a very small number of observed glyphs
- …